Collagen micro-architecture investigation in tumor sections by means of second-harmonic generation signal multiphasor analysis coupled with non-supervised machine learning techniques

Collagen organization changes with the tissue pathological conditions, like cancer, and can be monitored through Second-Harmonic Generation imaging, a label-free method sensitive to the fibrils microstructure. As a consequence, collagen can be exploited as an early-tumor diagnosis marker. Coupling a phasor-based method with a non-supervised machine learning algorithm, our protocol is able to map pixel by pixel crucial features of the collagen fibrils and enlighten different collagen organizations. Basing on these maps, our protocol can automatically discriminate, on fixed tumor sections, tumor area from the surrounding tissue with an accuracy of similar to 90%, opening the possibility to effectively assist histopathologists in cancer diagnosis.

Automated summary

This study demonstrates that collagen organization can serve as a marker for early tumor diagnosis. By utilizing a machine learning algorithm, accurate discrimination between tumor areas and surrounding tissue can be achieved.